Nesta análise serão observados os episódios da primeira até a quarta temporada das séries Vikings e Game of Thrones, utilizando dados do IMDB.

series = read_csv(here("data/series_from_imdb.csv"), 
                    progress = FALSE,
                    col_types = cols(.default = col_double(), 
                                     series_name = col_character(), 
                                     episode = col_character(), 
                                     url = col_character(),
                                     season = col_character())) %>% 
    filter(series_name %in% c("Vikings", "Game of Thrones")) %>%
    filter(season %in% 1:4)

Qual das duas séries é a mais bem avaliada?

p <- ggplot(data = series,
            mapping = aes(x = season_ep, 
            y = user_rating,
            size = user_votes,
            color = series_name))  + 
    geom_jitter(alpha = 0.5) +
    facet_wrap(~season) +
    labs(x = "Episódio",
         y = "Nota do Episódio",
         size = "Quantidade de Votos",
         color = "Nome da Série")
ggplotly(p)

Nos gráficos acima, que estão divididos por temporadas, podemos observar que em geral os pontos dos episódios que representam a série Game of Thrones estão acima dos pontos que representam a série Vikings, dessa forma, pode-se concluir que a dentre as duas, GOT é a mais bem avaliada pelos usuários do IMDB. Alguns outras características interessantes de serem observadas são: - A quantidade de votos que cada episódio recebe: Esta informação esta representada pelo tamanho dos pontos e pode ser quantificada ao se colocar o cursor em cima do ponto desejado no valor de “user_votes”. A diferença de tamanho dos pontos vermelhos para os azuis é expressiva. - Nas três primeiras temporadas de Game of Thrones os episódios anteriores ao último são sempre os mais altos no gráfico, ou seja, possuem melhor avaliação. - A partir da segunda temporada, podemos observar que os episódios de numéro dez, estão sempre acima da média.

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